1. Gastos (cálculos antiguos)

Gastos_casa %>% 
  dplyr::select(-Tiempo,-link) %>%
  dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>% 
  knitr::kable(format = "markdown", size=12)
fecha gasto monto gastador obs
11/12/2024 Comida 17738 Andrés compra lider despues viaje
12/12/2024 Comida 55616 Tami Supermercado
14/12/2024 Diosi 16276 Andrés antiparasitario
15/12/2024 Comida 23773 Tami Supermercado
18/12/2024 Comida 41554 Tami Supermercado
18/12/2024 VTR 22000 Andrés NA
20/12/2024 Plata basureros 10000 Tami NA
3/12/2024 Agua 15828 Andrés PAC AGUAS ANDIN 000000005687837
22/12/2024 Comida 46608 Tami Supermercado
10/12/2024 Otros 47484 Andrés viaje brasil
29/12/2024 Electricidad 27417 Andrés NA
29/12/2024 Comida 83284 Tami Supermercado
30/12/2024 Comida 30000 Andrés nueces almendras mix etc
30/12/2024 Otros 47484 Andrés viaje a brasil (duplicar para q cargue sobre tami)
4/1/2025 Diosi 53999 Andrés n y d pumpkin 7.5
4/1/2025 Comida 15260 Andrés NA
6/1/2025 Comida 40988 Tami Supermercado
8/1/2025 Pago cámaras MB 20000 Tami NA
13/1/2025 Comida 67387 Tami Supermercado
20/1/2025 Comida 21692 Andrés gnoccis
20/1/2025 Comida 86884 Tami Supermercado
21/1/2025 Comida 21525 Andrés piwen
23/1/2025 VTR 21990 Andrés NA
25/1/2025 Diosi 20000 Andrés arena diosi
27/1/2025 Comida 71516 Tami Supermercado
30/1/2025 Electricidad 55000 Andrés NA
6/2/2025 Comida 52730 Andrés supermercado (no cobre el otro de 25k pq muchas son cosas mías)
9/2/2025 Comida 12500 Andrés NA
31/3/2019 Comida 9000 Andrés NA
8/9/2019 Comida 24588 Andrés Super Lider

#para ver las diferencias depués de la diosi
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::group_by(gastador, fecha,.drop = F) %>% 
    dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>% 
    dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
    #dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de  diosi. Junio 24, 2019 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
    assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv) 

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")

par(mfrow=c(1,2)) 
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))

library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
  dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
  dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
  dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
  dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
  dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
  dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
#  dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
  #dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>% 
  dplyr::group_by(gastador_nombre, fecha_simp) %>%
  dplyr::summarise(monto_total=sum(monto)) %>%
  dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
  ggplot(aes(hover_css = "fill:none;")) +#, ) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
                       ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
 # guides(color = F)+
  theme_custom() +
  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
     theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

#  x <- girafe(ggobj = gg)
#  x <- girafe_options(x = x,
#                      opts_hover(css = "stroke:red;fill:orange") )
#  if( interactive() ) print(x)

#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"

#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )

x <- girafe(ggobj = gg)
x <- girafe_options(x,
  opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))
x
plot<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
    dplyr::group_by(month)%>%
    dplyr::summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = month, y = gasto_total)) +
      geom_point()+
      geom_line(size=1) +
      theme_custom() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Mes") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot)  
plot2<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = day, y = gasto_total)) +
      geom_line(size=1) +
      theme_custom() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Día") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot2)  
tsData <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
  data.frame()
tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))

tsData_gastos = decompose(tsData_gastos)

tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()

# Assuming your time series starts on "2019-03-03"
start_date <- as.Date("2019-03-03")
frequency <- 7  # Weekly data
num_periods <- length(tsData_gastos$x)  # Total number of periods in your time series

# Generate sequence of dates
dates <- tsData$day# seq.Date(from = start_date, by = "day", length.out = num_periods)

# Create a data frame from the decomposed time series object
tsData_gastos_df <- data.frame(
  day = dates,
  Actual = as.numeric(tsData_gastos$x),
  Seasonal = as.numeric(tsData_gastos$seasonal),
  Trend = as.numeric(tsData_gastos$trend),
  Random = as.numeric(tsData_gastos$random)
)

tsData_gastos_long <- tsData_gastos_df %>%
  pivot_longer(cols = c("Actual", "Seasonal", "Trend", "Random"), 
               names_to = "Component", values_to = "Value")

# Plotting with facet_wrap
ggplot(tsData_gastos_long, aes(x = day, y = Value)) +
  geom_line() +
  theme_bw() + 
  labs(title = "Descomposición de los Gastos Diarios", x = "Date", y = "Value") +
  scale_x_date(date_breaks = "3 months", date_labels = "%m %Y") +
  facet_wrap(~ Component, scales = "free_y", ncol=1) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
  theme(strip.text = element_text(size = 12))

#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
   #it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
   #ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan. 
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()

itsa_metro_region_quar2<-
        its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
                                 interrupt_var = "covid", 
                                 alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 

print(itsa_metro_region_quar2)
## [[1]]
## [1] "ITSA Model Fit"
## 
## $aov.result
## Anova Table (Type II tests)
## 
## Response: depvar
##                   Sum Sq  Df   F value Pr(>F)    
## interrupt_var 9.8063e+08   2    4.9252 0.0075 ** 
## lag_depvar    2.6110e+11   1 2622.7889 <2e-16 ***
## Residuals     8.0438e+10 808                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $tukey.result
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
## 
## $`x$interrupt_var`
##          diff       lwr      upr     p adj
## 1-0  7228.838 -1939.107 16396.78 0.1537583
## 2-0 31565.636 23307.131 39824.14 0.0000000
## 2-1 24336.798 19546.750 29126.85 0.0000000
## 
## 
## $data
##        depvar interrupt_var lag_depvar
## 2    19269.29             0   16010.00
## 3    24139.00             0   19269.29
## 4    23816.14             0   24139.00
## 5    26510.14             0   23816.14
## 6    23456.71             0   26510.14
## 7    24276.71             0   23456.71
## 8    18818.71             0   24276.71
## 9    18517.14             0   18818.71
## 10   15475.29             0   18517.14
## 11   16365.29             0   15475.29
## 12   12621.29             0   16365.29
## 13   12679.86             0   12621.29
## 14   13440.71             0   12679.86
## 15   15382.86             0   13440.71
## 16   13459.71             0   15382.86
## 17   14644.14             0   13459.71
## 18   13927.00             0   14644.14
## 19   22034.57             0   13927.00
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## 21   20390.57             0   20986.00
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## 707  95424.29             2   99637.71
## 708  98395.14             2   95424.29
## 709 115594.71             2   98395.14
## 710 114267.57             2  115594.71
## 711  88353.29             2  114267.57
## 712  88750.86             2   88353.29
## 713  78835.71             2   88750.86
## 714  75519.14             2   78835.71
## 715  73202.86             2   75519.14
## 716  53433.29             2   73202.86
## 717  48165.71             2   53433.29
## 718  52163.14             2   48165.71
## 719  49306.86             2   52163.14
## 720  36846.86             2   49306.86
## 721  43220.57             2   36846.86
## 722  38952.29             2   43220.57
## 723  41522.29             2   38952.29
## 724  39090.00             2   41522.29
## 725  28452.57             2   39090.00
## 726  32975.00             2   28452.57
## 727  33690.71             2   32975.00
## 728  26405.29             2   33690.71
## 729  47087.43             2   26405.29
## 730  49660.29             2   47087.43
## 731  47409.71             2   49660.29
## 732  53881.71             2   47409.71
## 733  45189.57             2   53881.71
## 734  45503.86             2   45189.57
## 735  54640.14             2   45503.86
## 736  39131.29             2   54640.14
## 737  35024.14             2   39131.29
## 738  44755.43             2   35024.14
## 739  41063.29             2   44755.43
## 740  42783.29             2   41063.29
## 741  45952.57             2   42783.29
## 742  44937.43             2   45952.57
## 743  40838.43             2   44937.43
## 744  48838.43             2   40838.43
## 745  43139.14             2   48838.43
## 746  67134.29             2   43139.14
## 747  73224.29             2   67134.29
## 748  68770.71             2   73224.29
## 749  59539.29             2   68770.71
## 750  82179.86             2   59539.29
## 751  74252.14             2   82179.86
## 752  73015.00             2   74252.14
## 753  56116.43             2   73015.00
## 754 111885.00             2   56116.43
## 755 131425.14             2  111885.00
## 756 136678.00             2  131425.14
## 757 115531.29             2  136678.00
## 758 118310.86             2  115531.29
## 759 117449.43             2  118310.86
## 760 115193.57             2  117449.43
## 761  61025.43             2  115193.57
## 762  43913.86             2   61025.43
## 763  46099.29             2   43913.86
## 764  44524.86             2   46099.29
## 765  42208.71             2   44524.86
## 766 166486.57             2   42208.71
## 767 171565.29             2  166486.57
## 768 200415.71             2  171565.29
## 769 204498.14             2  200415.71
## 770 197558.86             2  204498.14
## 771 195266.57             2  197558.86
## 772 203144.29             2  195266.57
## 773  85493.71             2  203144.29
## 774  74721.57             2   85493.71
## 775  36232.14             2   74721.57
## 776  40161.71             2   36232.14
## 777  40629.86             2   40161.71
## 778  45663.71             2   40629.86
## 779  39252.29             2   45663.71
## 780  39618.57             2   39252.29
## 781  39438.43             2   39618.57
## 782  44650.71             2   39438.43
## 783  38626.71             2   44650.71
## 784  38280.43             2   38626.71
## 785  44134.14             2   38280.43
## 786  47596.43             2   44134.14
## 787  45598.43             2   47596.43
## 788  42564.29             2   45598.43
## 789  45699.14             2   42564.29
## 790  49553.86             2   45699.14
## 791  50018.43             2   49553.86
## 792  43772.86             2   50018.43
## 793  39235.43             2   43772.86
## 794  39905.00             2   39235.43
## 795  40374.43             2   39905.00
## 796  34230.57             2   40374.43
## 797  34324.14             2   34230.57
## 798  33491.57             2   34324.14
## 799  33366.43             2   33491.57
## 800  46646.86             2   33366.43
## 801  49770.86             2   46646.86
## 802  57339.86             2   49770.86
## 803  59799.14             2   57339.86
## 804  53577.14             2   59799.14
## 805  61775.29             2   53577.14
## 806  70627.86             2   61775.29
## 807  57888.43             2   70627.86
## 808  49960.71             2   57888.43
## 809  42923.71             2   49960.71
## 810  47284.86             2   42923.71
## 811  52284.86             2   47284.86
## 812  50191.00             2   52284.86
## 813  36465.86             2   50191.00
## 
## $alpha
## [1] 0.05
## 
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
## 
## $group.means
##   interrupt_var count     mean      s.d.
## 1             0    37 22066.04  6308.636
## 2             1   120 29463.10  9187.258
## 3             2   656 53799.90 22449.362
## 
## $dependent
##   [1]  19269.29  24139.00  23816.14  26510.14  23456.71  24276.71  18818.71
##   [8]  18517.14  15475.29  16365.29  12621.29  12679.86  13440.71  15382.86
##  [15]  13459.71  14644.14  13927.00  22034.57  20986.00  20390.57  22554.14
##  [22]  21782.57  22529.57  24642.71  17692.29  19668.29  28640.00  28706.00
##  [29]  28331.57  25617.86  27223.29  31622.57  32021.43  33634.57  30784.86
##  [36]  34770.57  38443.00  35073.00  31422.29  30103.29  19319.29  27926.29
##  [43]  30715.43  31962.29  39790.14  39211.57  44548.57  49398.00  41039.00
##  [50]  34821.29  29123.57  21275.71  28476.14  24561.86  20323.57  25370.00
##  [57]  26811.86  27151.86  27623.29  22896.57  41889.29  44000.14  38558.00
##  [64]  43373.86  49001.00  61213.29  58939.57  42046.86  39191.71  42646.43
##  [71]  36121.57  30915.57  20273.43  23938.29  19274.29  21662.29  15819.00
##  [78]  18126.14  17240.71  16127.71  13917.14  15379.86  19510.14  24567.29
##  [85]  25700.43  25729.00  26435.00  31157.14  29818.43  30962.43  28746.71
##  [92]  27830.71  28252.14  28717.57  21365.43  24816.86  16838.57  15529.14
##  [99]  13286.29  13629.43  14404.86  19524.86  18475.71  22495.00  22254.57
## [106]  24173.29  27466.43  24602.43  20531.14  20846.43  23875.71  36312.71
## [113]  34244.00  36347.43  39779.71  42018.71  39372.57  33444.00  29255.86
## [120]  31640.14  29671.14  31023.71  39723.43  39314.14  38239.86  34649.43
## [127]  36688.43  42867.57  42226.86  32155.14  33603.00  37254.43  33145.57
## [134]  31299.43  30252.00  26310.71  27929.86  27666.14  25017.57  27335.00
## [141]  25760.71  18436.86  21906.00  19418.14  22826.14  23444.29  25264.86
## [148]  25473.29  27366.86  28855.86  32326.86  27141.43  26297.71  23499.14
## [155]  30246.29  39931.86  38020.43  35004.00  40750.86  42363.29  46273.57
## [162]  41083.29  35711.29  41921.71  60583.29  63115.57  61300.14  57666.43
## [169]  55834.00  58927.71  57810.57  48987.14  52219.29  56503.57  56545.00
## [176]  64705.57  53833.29  50114.00  39592.43  29907.29  33923.29  45489.00
## [183]  44866.29  51680.57  58257.00  70600.57  76648.00  69430.14  69651.57
## [190]  77745.14  72795.86  67670.71  55357.86  48524.00  50154.43  45111.57
## [197]  36147.00  43501.57  41472.43  41058.00  41605.57  49382.86  59558.57
## [204]  59134.57  61109.00  63004.43  67344.29  78180.86  69117.86  55597.57
## [211]  49426.14  39119.43  35636.86  39201.14  27777.00  47207.00  55587.29
## [218]  56619.71  82679.86  91259.57  93552.71 102242.71  91884.00  85013.86
## [225]  84535.29  80700.43  79740.57  85163.14  86724.86  80355.00  74875.14
## [232]  81347.00  66062.43  56946.43  47732.14  38129.71  42928.29  45392.57
## [239]  37895.43  30660.29  42430.86  35845.14  40350.43  31494.71  30013.29
## [246]  34197.57  37430.14  26932.43  33729.86  38081.43  44028.00  47139.71
## [253]  46558.86  58350.57  78380.00  78168.29  70510.86  72207.14  67881.00
## [260]  69536.43  62390.71  50113.14  45565.57  45805.29  41348.57  51426.86
## [267]  47160.57  51907.43  49751.43  54407.43  54746.29  61634.57  58926.43
## [274]  69999.29  63044.86  63285.29  61395.43  67969.43  60792.57  56859.14
## [281]  44899.43  43064.14  62790.29  69120.71  69589.43  66633.29  65588.57
## [288]  70168.57  74644.71  52891.00  41560.57  34704.86  46520.00  50231.00
## [295]  49216.71  76914.86  83720.71  84485.00  89765.00  87702.86  82013.86
## [302]  85982.43  57248.43  52968.43  52601.86  45493.29  42298.86  46423.71
## [309]  37898.00  36435.14  30209.57  34541.86  33604.71  37990.71  35683.43
## [316]  65201.86  62730.57  64589.14  73744.86  76477.71 105647.43 103790.29
## [323]  76122.29  74746.14  72865.71  63652.57  60358.29  25957.14  30178.43
## [330]  30681.57  33337.29  32582.71  39184.43  40415.71  34975.43  34076.14
## [337]  34221.14  28862.57  35729.86  36489.29  36785.14  37787.71  39832.14
## [344]  41917.86  41633.57  33557.00  22759.57  28877.86  27574.00  27104.71
## [351]  24376.14  29732.29  34030.00  39139.71  37066.57  38509.29  40957.29
## [358]  49423.00  50053.29  50284.14  53103.86  50223.00  49587.14  41167.71
## [365]  37958.71  33582.29  31039.43  26526.57  34869.43  37487.43  46514.43
## [372]  39613.43  38980.57  37306.14  36771.29  26317.00  31580.71  23626.57
## [379]  33035.71  44864.57  48946.14  46969.57  49249.57  56370.14  67228.71
## [386]  59457.29  53124.71  52814.14  61262.00  61861.14  71784.71  59313.29
## [393]  61107.00  60603.43  60012.57  58280.43  56862.71  41704.43  51533.00
## [400]  50388.71  49205.29  56533.29  47996.14  47207.57  45292.00  40343.43
## [407]  39004.86  36788.43  30027.57  39040.14  42390.14  36291.14  30668.29
## [414]  47693.00  52094.43  56592.57  47971.43  43762.43  42246.71  46352.43
## [421]  33094.86  32784.86  26212.43  32611.57  42144.86  50034.86  46332.00
## [428]  42976.29  39456.29  39328.29  35296.14  30875.43  27709.00  29513.29
## [435]  31630.43  29346.14  34916.86  42020.86  38303.00  37966.43  41408.14
## [442]  38988.14  43555.29  38114.00  27847.86  26517.00  39518.29  39153.71
## [449]  45623.14  40627.43  41027.71  42882.86  47139.43  35547.57  41099.00
## [456]  35859.57  44524.57  48554.29  51554.29  47810.29  50490.00  50720.71
## [463]  52720.71  52145.57  55515.57  52457.00  58239.57  50523.57  47788.57
## [470]  46170.00  42305.57  46605.57  55149.57  48769.57  50719.43  44753.71
## [477]  42898.00  46141.14  34022.57  26651.86  28791.86  31879.00  33584.71
## [484]  34690.43  27410.43  41755.00  49379.57  57198.86  51144.57  56677.43
## [491]  65416.43  69779.71  54046.00  43259.57  40998.57  41368.57  42274.29
## [498]  35962.71  38709.00  44778.14  51282.43  52094.86  52221.43  45011.43
## [505]  46545.43  42263.00  45417.43  45034.71  37840.57  39135.43  38191.14
## [512]  39456.86  42479.14  34282.57  28878.43  56227.14  65569.43  69751.29
## [519]  62171.71  63705.14  79257.86  87244.71  58568.00  52695.29  48911.00
## [526]  53924.00  53358.86  42121.14  47835.71  62329.29  56056.86  59946.43
## [533]  64511.57  61137.43  55448.71  47964.43  46425.71  55512.00  55226.29
## [540]  46709.14  49254.71  49056.29  49850.57  39145.71  29799.43  34769.86
## [547]  44061.57  43829.14  45782.00  38924.57  49242.43  50565.00  38864.43
## [554]  49786.71  58787.86  58060.86  62179.43  57333.86  70797.00  89901.71
## [561]  78558.14  65466.00  70525.00  68377.86  69736.29  60085.86  41757.00
## [568]  49780.29  56540.29  57894.29  60270.29  61011.00  57721.43  71741.00
## [575]  59576.00  52390.29  61092.29  62814.00  54908.29  62082.00  57017.71
## [582]  53634.43  69169.00  52488.14  60895.57  59856.57  52670.00  51874.57
## [589]  52190.57  41562.43  44764.14  38612.71  43473.14  53505.00  45870.86
## [596]  52578.00  55300.00  61789.71  57391.71  62902.29  53250.43  55402.57
## [603]  56291.29  58933.57  59590.71  59065.00  52399.57  60483.43  58262.71
## [610]  54939.71  51169.00  43113.29  56289.71  60739.86  50363.14  62270.86
## [617]  67061.57  59609.00  85054.00  68023.29  59242.29  61535.14  56215.86
## [624]  45152.29  57409.57  35151.43  34991.43  45944.71  57944.71  55706.29
## [631]  88593.71  77359.43  79878.71  81753.00  75716.00  67381.43  63528.57
## [638]  49682.86  47815.00  46546.14  44808.71  42959.57  46023.86  51309.57
## [645]  68447.29  84959.29  81666.29  82700.86  89422.14 104812.71  98812.71
## [652]  64779.86  61862.86  58376.43  59503.57  55429.43  44454.57  47184.00
## [659]  52126.71  51202.00  64437.14  64297.14  64628.57  51413.14  52969.43
## [666]  54135.29  48799.43  41907.86  45382.00  42633.29  46624.71  44051.86
## [673]  35852.86  29737.71  29734.86  32881.71  38298.57  40886.14  38601.86
## [680]  38628.86  39142.57  32666.14  39911.57  39336.29  39678.86  41963.14
## [687]  54220.57  63901.86  73116.00  60863.86  56293.86  52725.00  58625.00
## [694]  47513.00  40300.14  33312.43  29556.71  27816.71  34120.29  32132.57
## [701]  32902.57  39694.14  72501.29  79551.14  99637.71  95424.29  98395.14
## [708] 115594.71 114267.57  88353.29  88750.86  78835.71  75519.14  73202.86
## [715]  53433.29  48165.71  52163.14  49306.86  36846.86  43220.57  38952.29
## [722]  41522.29  39090.00  28452.57  32975.00  33690.71  26405.29  47087.43
## [729]  49660.29  47409.71  53881.71  45189.57  45503.86  54640.14  39131.29
## [736]  35024.14  44755.43  41063.29  42783.29  45952.57  44937.43  40838.43
## [743]  48838.43  43139.14  67134.29  73224.29  68770.71  59539.29  82179.86
## [750]  74252.14  73015.00  56116.43 111885.00 131425.14 136678.00 115531.29
## [757] 118310.86 117449.43 115193.57  61025.43  43913.86  46099.29  44524.86
## [764]  42208.71 166486.57 171565.29 200415.71 204498.14 197558.86 195266.57
## [771] 203144.29  85493.71  74721.57  36232.14  40161.71  40629.86  45663.71
## [778]  39252.29  39618.57  39438.43  44650.71  38626.71  38280.43  44134.14
## [785]  47596.43  45598.43  42564.29  45699.14  49553.86  50018.43  43772.86
## [792]  39235.43  39905.00  40374.43  34230.57  34324.14  33491.57  33366.43
## [799]  46646.86  49770.86  57339.86  59799.14  53577.14  61775.29  70627.86
## [806]  57888.43  49960.71  42923.71  47284.86  52284.86  50191.00  36465.86
## 
## $interrupt_var
##   [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
##  [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##  [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [741] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [778] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
## 
## $residuals
##            2            3            4            5            6            7 
##   2023.87551   4042.50994   -540.15917   2436.26206  -2973.75927    517.24580 
##            8            9           10           11           12           13 
##  -5658.05415  -1185.20712  -3963.26284   -412.37994  -4934.91281  -1601.25478 
##           14           15           16           17           18           19 
##   -891.63335    384.94513  -3237.09891   -370.38952  -2123.61842   6611.27805 
##           20           21           22           23           24           25 
##  -1529.44043  -1207.62481   1476.80135  -1187.36725    234.56952   1694.26962 
##           26           27           28           29           30           31 
##  -7104.64343    951.28593   8194.48228    412.42105    -19.74142  -2405.92206 
##           32           33           34           35           36           37 
##   1573.34447   4568.27185   1118.82725   2383.06742  -1877.75332   4600.76574 
##           38           39           40           41           42           43 
##   4299.53344  -2282.94562  -2985.73206  -1111.24770 -10741.44449   7298.92462 
##           44           45           46           47           48           49 
##   2559.04226   1366.07985   8103.24118    677.20446   6520.31331   6701.16891 
##           50           51           52           53           54           55 
##  -5899.89801  -4805.52658  -5064.25776  -7928.00493   6137.38406  -4075.52002 
##           56           57           58           59           60           61 
##  -4889.76067   3864.13391    891.59720    -29.67604    144.33548  -4994.76380 
##           62           63           64           65           66           67 
##  18132.67234   3629.53800  -3659.08989   5917.31458   7331.75732  14621.66585 
##           68           69           70           71           72           73 
##   1665.18090 -13238.58808  -1316.72713   4635.54061  -4911.34871  -4409.69054 
##           74           75           76           77           78           79 
## -10497.85346   2476.28240  -5393.57352   1074.28864  -6857.91449    560.67782 
##           80           81           82           83           84           85 
##  -2342.93789  -2681.40390  -3918.37186   -521.94687   2328.82069   3772.97118 
##           86           87           88           89           90           91 
##    482.34779   -480.30433    200.70264   4305.26773  -1164.16953   1150.87888 
##           92           93           94           95           96           97 
##  -2065.55632  -1043.34687    179.35823    276.13962  -7483.13969   2399.62023 
##           98           99          100          101          102          103 
##  -8597.82346  -2928.19844  -4025.62503  -1720.52935  -1245.26706   3196.42211 
##          104          105          106          107          108          109 
##  -2331.47166   2605.55811  -1150.76491    978.26572   2593.00167  -3151.69492 
##          110          111          112          113          114          115 
##  -4717.67935   -841.01187   1912.47576  11699.58979  -1248.46533   2664.58355 
##          116          117          118          119          120          121 
##   4256.88242   3493.46977  -1111.25185  -4725.09384  -3727.18305   2320.70592 
##          122          123          124          125          126          127 
##  -1733.96241   1341.00366   8857.54792    838.13461    121.87405  -2528.81661 
##          128          129          130          131          132          133 
##   2650.93246   7046.44776   1000.49095  -8510.75465   1747.39538   4132.30217 
##          134          135          136          137          138          139 
##  -3170.66416  -1422.55943   -855.06339  -3880.10463   1186.70170   -493.36758 
##          140          141          142          143          144          145 
##  -2911.25334   1723.02908  -1878.44127  -7825.18248   2050.54863  -3471.96217 
##          146          147          148          149          150          151 
##   2112.30588   -250.71984   1029.12740   -354.99988   1356.24740   1188.83436 
##          152          153          154          155          156          157 
##   3357.32262  -4864.38416  -1172.11350  -3232.64076   5962.56935   9746.03683 
##          158          159          160          161          162          163 
##  -3643.69517  -4988.09006   3397.40619    -17.26322   2482.54087  -6128.29088 
##          164          165          166          167          168          169 
##  -6960.05712   3949.56089  17178.52243   3386.48587   -644.07492  -2689.73210 
##          170          171          172          173          174          175 
##  -1343.54717   3353.09508   -470.29303  -8316.49414   2633.99613   4090.94536 
##          176          177          178          179          180          181 
##    384.66914   8509.00068  -9501.79416  -3710.48231 -10978.58610 -11459.92083 
##          182          183          184          185          186          187 
##   1028.21636   9080.91039  -1658.98229   5700.02651   6315.61751  12906.41834 
##          188          189          190          191          192          193 
##   8156.23328  -4351.64852   2183.64417  10083.51962  -1945.66657  -2741.39187 
##          194          195          196          197          198          199 
## -10570.99935  -6634.11037    974.27599  -5494.80838 -10048.11008   5148.27433 
##          200          201          202          203          204          205 
##  -3314.32429  -1953.74790  -1043.65257   6254.64173   9627.12838    301.86087 
##          206          207          208          209          210          211 
##   2647.18601   2815.47126   5497.29082  12537.54601  -6004.81027 -11597.18202 
##          212          213          214          215          216          217 
##  -5941.65903 -10849.87892  -5316.58980   1294.09630 -13247.92701  16175.41093 
##          218          219          220          221          222          223 
##   7559.18684   1260.90989  26417.92962  12201.37660   6989.36259  13673.42203 
##          224          225          226          227          228          229 
##  -4286.92222  -2095.71722   3435.41030     19.18640   2413.89370   8676.10596 
##          230          231          232          233          234          235 
##   5494.39315  -2241.58300  -2149.36915   9116.02612 -11829.84142  -7575.57028 
##          236          237          238          239          240          241 
##  -8815.57996 -10357.75647   2840.59732   1107.30371  -8545.48796  -9222.45984 
##          242          243          244          245          246          247 
##   8877.09649  -8004.99622   2261.18283 -10535.55734  -4270.39637   1209.77793 
##          248          249          250          251          252          253 
##    782.12017 -12543.30549   3437.06203   1842.54178   3982.54983   1892.46485 
##          254          255          256          257          258          259 
##  -1410.38315  10889.43943  20603.99474   2871.41689  -4600.81334   3793.85425 
##          260          261          262          263          264          265 
##  -2016.12478   3423.62338  -5170.18704 -11197.00176  -5004.69346   -786.96361 
##          266          267          268          269          270          271 
##  -5453.36941   8523.45403  -4558.87296   3919.94337  -2388.39858   4153.57544 
##          272          273          274          275          276          277 
##    419.56847   7011.43686  -1722.27552  11719.54594  -4920.93127   1402.92568 
##          278          279          280          281          282          283 
##   -697.24780   7529.91614  -5397.58719  -3053.01665 -11571.94054  -2945.39398 
##          284          285          286          287          288          289 
##  18386.17614   7461.04215   2392.17582   -973.97769    567.21184   6061.08197 
##          290          291          292          293          294          295 
##   6530.84218 -19138.40510 -11439.64076  -8383.99425   9428.22607   2803.85852 
##          296          297          298          299          300          301 
##  -1456.64685  27128.74856   9705.48802   4516.30911   9127.74556   2446.89083 
##          302          303          304          305          306          307 
##  -1438.23726   7506.82127 -24698.71055  -3843.47057   -466.08616  -7253.99702 
##          308          309          310          311          312          313 
##  -4230.15981   2689.04301  -9444.91497  -3449.85207  -8395.78038   1382.36153 
##          314          315          316          317          318          319 
##  -3344.47441   1861.29696  -4282.66873  27254.07195  -1038.63737   2981.70615 
##          320          321          322          323          324          325 
##  10511.62386   5235.46466  32014.59566   4641.06912 -21402.38396   1424.22323 
##          326          327          328          329          330          331 
##    747.58393  -6820.64268  -2055.67606 -33575.12260    738.77053  -2450.68178 
##          332          333          334          335          336          337 
##   -235.09475  -3312.76828   3949.01192   -594.59180  -7111.95212  -3252.31506 
##          338          339          340          341          342          343 
##  -2320.65946  -7806.07051   3748.65791  -1499.11315  -1867.57074  -1123.80213 
##          344          345          346          347          348          349 
##     43.62103    340.95908  -1767.81780  -9595.70858 -13328.10754   2235.29401 
##          350          351          352          353          354          355 
##  -4420.57050  -3749.29931  -6067.36022   1675.61696   1288.01295   2638.27571 
##          356          357          358          359          360          361 
##  -3904.62056   -648.41205    537.56492   6861.87643     86.72746   -233.76163 
##          362          363          364          365          366          367 
##   2384.00898  -2963.41026  -1079.22023  -8942.42892  -4786.48295  -6355.81940 
##          368          369          370          371          372          373 
##  -5070.36922  -7358.84673   4931.65944    251.69479   6988.58348  -7808.83927 
##          374          375          376          377          378          379 
##  -2405.00499  -3525.83796  -2595.97860 -12582.39480   1826.26903 -10732.33968 
##          380          381          382          383          384          385 
##   5634.73767   9232.89029   2967.09752  -2579.85316   1429.16463   6555.29229 
##          386          387          388          389          390          391 
##  11185.10087  -6084.92861  -5619.39598   -390.51232   8329.01906   1538.34788 
##          392          393          394          395          396          397 
##  10937.81547 -10214.31712   2488.85461    416.22078    265.86578   -949.42122 
##          398          399          400          401          402          403 
##   -851.93309 -14770.06467   8318.30870  -1423.57921  -1606.03694   6757.17435 
##          404          405          406          407          408          409 
##  -8190.18075  -1510.83493  -2736.59874  -6009.51249  -3019.29119  -4064.79632 
##          410          411          412          413          414          415 
##  -8886.81919   6039.85284   1506.05157  -7523.38115  -7811.10122  14132.24130 
##          416          417          418          419          420          421 
##   3641.19826   4289.16490  -8266.75558  -4934.35881  -2768.22494   2663.36957 
##          422          423          424          425          426          427 
## -14185.70022  -2898.55957  -9199.81377   2948.60066   6884.19753   6434.89829 
##          428          429          430          431          432          433 
##  -4169.78398  -4286.40163  -4870.97030  -1919.82905  -5840.00313  -6733.57611 
##          434          435          436          437          438          439 
##  -6032.95817  -1458.81994   -919.98691  -5056.25612   2512.65089   4743.63493 
##          440          441          442          443          444          445 
##  -5188.48910  -2272.84255   1463.28963  -3967.37072   2716.68174  -6719.73976 
##          446          447          448          449          450          451 
## -12226.08508  -4576.57178   9588.88925  -2148.63535   4639.70428  -6015.18171 
##          452          453          454          455          456          457 
##  -1244.86476    260.12575   2893.89977 -12421.41893   3270.05666  -6825.51757 
##          458          459          460          461          462          463 
##   6422.70420   2872.65743   2347.64053  -4020.62759   1934.17330   -179.20866 
##          464          465          466          467          468          469 
##   1618.97263   -705.68231   3167.42738  -2839.07189   5619.00336  -7155.33591 
##          470          471          472          473          474          475 
##  -3140.71833  -2366.83199  -4815.40544   2865.02679   7647.57583  -6206.33976 
##          476          477          478          479          480          481 
##   1324.46090  -6346.90269  -2984.07239   1882.36775 -13073.16248  -9843.08324 
##          482          483          484          485          486          487 
##  -1255.50642    -40.34148  -1035.12405  -1421.49363  -9668.72388  11044.07150 
##          488          489          490          491          492          493 
##   6120.64247   7270.28829  -5623.96481   5204.91532   9104.01513   5822.80783 
##          494          495          496          497          498          499 
## -13727.71698 -10750.98399  -3576.49047  -1228.66707   -646.61252  -7750.46299 
##          500          501          502          503          504          505 
##    516.90794   4183.72079   5378.98715    501.75252    -82.35285  -7403.07197 
##          506          507          508          509          510          511 
##    437.91906  -5186.38527   1714.12354  -1427.94618  -8287.30740   -699.33036 
##          512          513          514          515          516          517 
##  -2776.30018   -684.56626   1230.52824  -9609.80587  -7843.94838  24232.07250 
##          518          519          520          521          522          523 
##   9650.90527   5660.54158  -5577.13463   2586.56981  16797.90820  11179.93462 
##          524          525          526          527          528          529 
## -24483.33117  -5270.91650  -3918.00994   4405.31684   -544.97804 -11288.33020 
##          530          531          532          533          534          535 
##   4256.49965  13751.21522  -5199.55246   4176.86373   5339.58050  -2027.94864 
##          536          537          538          539          540          541 
##  -4765.11109  -7273.15963  -2264.94981   8167.33552    -66.66205  -8333.87461 
##          542          543          544          545          546          547 
##   1662.11895   -763.06360    204.79871 -11194.86466 -11177.01196   1969.13647 
##          548          549          550          551          552          553 
##   6912.93836  -1447.47339    708.70205  -7857.00009   8459.43409    756.39769 
##          554          555          556          557          558          559 
## -12101.10108   9056.33013   8503.13762    -97.66644   4656.85263  -3791.46402 
##          560          561          562          563          564          565 
##  13910.37170  21238.12049  -6817.41510  -9986.70037   6524.73064    -47.80301 
##          566          567          568          569          570          571 
##   3188.85174  -7649.87043 -17536.95692   6519.60724   6261.18962   1701.83881 
##          572          573          574          575          576          577 
##   2893.41914   1555.71308  -2381.80264  14515.34125  -9913.36350  -6457.67059 
##          578          579          580          581          582          583 
##   8530.07637   2639.66361  -6772.13064   7317.15495  -4022.38065  -2975.65186 
##          584          585          586          587          588          589 
##  15518.46918 -14751.34811   8247.76095   -145.68795  -6423.38785   -932.31968 
##          590          591          592          593          594          595 
##     79.48627 -10825.07950   1673.66692  -7278.48056   2962.94730   8743.11520 
##          596          597          598          599          600          601 
##  -7666.45526   5718.70016   2573.58645   6682.21480  -3392.70195   5965.04653 
##          602          603          604          605          606          607 
##  -8507.21624   2087.94692   1094.06125   2958.93878   1304.72626    204.17229 
##          608          609          610          611          612          613 
##  -6001.38454   7913.09649  -1379.02064  -2759.43741  -3623.33738  -8380.59658 
##          614          615          616          617          618          619 
##  11842.61670   4766.59911  -9502.90451  11481.90325   5856.27257  -5787.00510 
##          620          621          622          623          624          625 
##  26177.17675 -13111.70491  -6994.98477   2979.10514  -4345.87120 -10756.36534 
##          626          627          628          629          630          631 
##  11178.84628 -21801.43126  -2490.98641   8602.26027  11020.80746  -1714.69356 
##          632          633          634          635          636          637 
##  33130.81392  -6871.94849   5474.59648   5145.12180  -2531.42094  -5585.09017 
##          638          639          640          641          642          643 
##  -2147.23064 -12622.63488  -2378.86986  -2013.80769  -2641.29581  -2970.61254 
##          644          645          646          647          648          649 
##   1711.22205   4316.43391  16830.43771  18351.11867    614.14698   4529.29004 
##          650          651          652          653          654          655 
##  10345.57816  19856.66433    393.66905 -28390.65187  -1537.20461  -2471.96982 
##          656          657          658          659          660          661 
##   1704.94748  -3355.17040 -10766.14648   1563.60463   4118.73480  -1129.64865 
##          662          663          664          665          666          667 
##  12914.39361   1196.87249   1650.76691 -11854.58081   1261.98084   1066.46766 
##          668          669          670          671          672          673 
##  -5289.23006  -7513.22824   1989.35833  -3798.38339   2597.49959  -3466.88379 
##          674          675          676          677          678          679 
##  -9415.26147  -8358.27958  -3011.87859    137.47786   2801.60272    650.74566 
##          680          681          682          683          684          685 
##  -3897.03379  -1871.84107  -1381.74519  -8307.54844   4603.17513  -2310.09297 
##          686          687          688          689          690          691 
##  -1464.28689    520.33240  10779.56825   9738.59432  10483.97405  -9828.29591 
##          692          693          694          695          696          697 
##  -3680.65997  -3251.88202   5769.99731 -10503.06331  -7995.63137  -8673.85532 
##          698          699          700          701          702          703 
##  -6317.02435  -4771.69060   3053.95633  -4447.84512  -1939.08004   4178.92924 
##          704          705          706          707          708          709 
##  31045.10398   9396.71484  23316.38117   1532.10297   8188.68220  22789.47840 
##          710          711          712          713          714          715 
##   6416.90660 -18336.45289   4729.79642  -5533.12444   -176.36479    408.54037 
##          716          717          718          719          720          721 
## -17334.84614  -5308.86561   3296.40287  -3056.65762 -13018.10445   4255.07007 
##          722          723          724          725          726          727 
##  -5588.66070    715.04800  -3965.36073 -12475.13269   1352.45075  -1887.85667 
##          728          729          730          731          732          733 
##  -9799.36063  17255.75486   1736.78267  -2764.41108   5676.28986  -8677.27407 
##          734          735          736          737          738          739 
##   -759.48391   8101.87848 -15398.99975  -5939.67619   7384.35753  -4820.28625 
##          740          741          742          743          744          745 
##    129.43800   1794.14333  -1993.35134  -5204.34899   6381.27600  -6316.05801 
##          746          747          748          749          750          751 
##  22664.56943   7764.67335  -2016.16234  -7351.80242  23364.01688  -4368.67395 
##          752          753          754          755          756          757 
##   1328.99913 -14487.37412  56063.32469  26819.57300  14979.57220 -10762.11058 
##          758          759          760          761          762          763 
##  10515.67684   7222.80138   5720.48408 -46474.33412 -16201.99555    951.88344 
##          764          765          766          767          768          769 
##  -2534.26197  -3473.16393 122830.75316  19196.66159  43604.45418  22449.79618 
##          770          771          772          773          774          775 
##  11939.38142  15717.27778  25600.18284 -98941.46672  -6798.06192 -35864.49349 
##          776          777          778          779          780          781 
##   1733.93796  -1235.33552   3389.01083  -7425.81467  -1451.09310  -1951.64660 
##          782          783          784          785          786          787 
##   3418.22017  -7165.25823  -2242.01357   3914.61623   2256.33003  -2770.32530 
##          788          789          790          791          792          793 
##  -4056.70559   1732.28633   2844.76541    -62.59776  -6714.55585  -5788.63311 
##          794          795          796          797          798          799 
##  -1149.91864  -1266.20172  -7820.69434  -2352.74678  -3267.17038  -2664.01636 
##          800          801          802          803          804          805 
##  10725.88169   2232.74661   7069.00875   2907.26602  -5466.00901   8174.86592 
##          806          807          808          809          810          811 
##   9856.06238 -10627.20649  -7411.02872  -7513.21279   3003.58831   4188.65226 
##          812          813 
##  -2278.98507 -14172.51375 
## 
## $fitted.values
##         2         3         4         5         6         7         8         9 
##  17245.41  20096.49  24356.30  24073.88  26430.47  23759.47  24476.77  19702.35 
##        10        11        12        13        14        15        16        17 
##  19438.55  16777.67  17556.20  14281.11  14332.35  14997.91  16696.81  15014.53 
##        18        19        20        21        22        23        24        25 
##  16050.62  15423.29  22515.44  21598.20  21077.34  22969.94  22295.00  22948.44 
##        26        27        28        29        30        31        32        33 
##  24796.93  18717.00  20445.52  28293.58  28351.31  28023.78  25649.94  27054.30 
##        34        35        36        37        38        39        40        41 
##  30902.60  31251.50  32662.61  30169.81  34143.47  37355.95  34408.02  31214.53 
##        42        43        44        45        46        47        48        49 
##  30060.73  20627.36  28156.39  30596.21  31686.90  38534.37  38028.26  42696.83 
##        50        51        52        53        54        55        56        57 
##  46938.90  39626.81  34187.83  29203.72  22338.76  28637.38  25213.33  21505.87 
##        58        59        60        61        62        63        64        65 
##  25920.26  27181.53  27478.95  27891.34  23756.61  40370.60  42217.09  37456.54 
##        66        67        68        69        70        71        72        73 
##  41669.24  46591.62  57274.39  55285.45  40508.44  38010.89  41032.92  35325.26 
##        74        75        76        77        78        79        80        81 
##  30771.28  21462.00  24667.86  20588.00  22676.91  17565.47  19583.65  18809.12 
##        82        83        84        85        86        87        88        89 
##  17835.51  15901.80  17181.32  20794.31  25218.08  26209.30  26234.30  26851.88 
##        90        91        92        93        94        95        96        97 
##  30982.60  29811.55  30812.27  28874.06  28072.78  28441.43  28848.57  22417.24 
##        98        99       100       101       102       103       104       105 
##  25436.39  18457.34  17311.91  15349.96  15650.12  16328.44  20807.19  19889.44 
##       106       107       108       109       110       111       112       113 
##  23405.34  23195.02  24873.43  27754.12  25248.82  21687.44  21963.24  24613.12 
##       114       115       116       117       118       119       120       121 
##  35492.47  33682.85  35522.83  38525.24  40483.82  38169.09  32983.04  29319.44 
##       122       123       124       125       126       127       128       129 
##  31405.11  29682.71  30865.88  38476.01  38117.98  37178.25  34037.50  35821.12 
##       130       131       132       133       134       135       136       137 
##  41226.37  40665.90  31855.60  33122.13  36316.24  32721.99  31107.06  30190.82 
##       138       139       140       141       142       143       144       145 
##  26743.16  28159.51  27928.82  25611.97  27639.16  26262.04  19855.45  22890.11 
##       146       147       148       149       150       151       152       153 
##  20713.84  23695.01  24235.73  25828.29  26010.61  27667.02  28969.53  32005.81 
##       154       155       156       157       158       159       160       161 
##  27469.83  26731.78  24283.72  30185.82  41664.12  39992.09  37353.45  42380.55 
##       162       163       164       165       166       167       168       169 
##  43791.03  47211.58  42671.34  37972.15  43404.76  59729.09  61944.22  60356.16 
##       170       171       172       173       174       175       176       177 
##  57177.55  55574.62  58280.86  57303.64  49585.29  52412.63  56160.33  56196.57 
##       178       179       180       181       182       183       184       185 
##  63335.08  53824.48  50571.01  41367.21  32895.07  36408.09  46525.27  45980.54 
##       186       187       188       189       190       191       192       193 
##  51941.38  57694.15  68491.77  73781.79  67467.93  67661.62  74741.52  70412.11 
##       194       195       196       197       198       199       200       201 
##  65928.86  55158.11  49180.15  50606.38  46195.11  38353.30  44786.75  43011.75 
##       202       203       204       205       206       207       208       209 
##  42649.22  43128.22  49931.44  58832.71  58461.81  60188.96  61846.99  65643.31 
##       210       211       212       213       214       215       216       217 
##  75122.67  67194.75  55367.80  49969.31  40953.45  37907.05  41024.93  31031.59 
##       218       219       220       221       222       223       224       225 
##  48028.10  55358.80  56261.93  79058.19  86563.35  88569.29  96170.92  87109.57 
##       226       227       228       229       230       231       232       233 
##  81099.88  80681.24  77326.68  76487.04  81230.46  82596.58  77024.51  72230.97 
##       234       235       236       237       238       239       240       241 
##  77892.27  64522.00  56547.72  48487.47  40087.69  44285.27  46440.92  39882.75 
##       242       243       244       245       246       247       248       249 
##  33553.76  43850.14  38089.25  42030.27  34283.68  32987.79  36648.02  39475.73 
##       250       251       252       253       254       255       256       257 
##  30292.80  36238.89  40045.45  45247.25  47969.24  47461.13  57776.01  75296.87 
##       258       259       260       261       262       263       264       265 
##  75111.67  68413.29  69897.12  66112.81  67560.90  61310.14  50570.26  46592.25 
##       266       267       268       269       270       271       272       273 
##  46801.94  42903.40  51719.44  47987.49  52139.83  50253.85  54326.72  54623.13 
##       274       275       276       277       278       279       280       281 
##  60648.70  58279.74  67965.79  61882.36  62092.68  60439.51  66190.16  59912.16 
##       282       283       284       285       286       287       288       289 
##  56471.37  46009.54  44404.11  61659.67  67197.25  67607.26  65021.36  64107.49 
##       290       291       292       293       294       295       296       297 
##  68113.87  72029.41  53000.21  43088.85  37091.77  47427.14  50673.36  49786.11 
##       298       299       300       301       302       303       304       305 
##  74015.23  79968.69  80637.25  85255.97  83452.09  78475.61  81947.14  56811.90 
##       306       307       308       309       310       311       312       313 
##  53067.94  52747.28  46529.02  43734.67  47342.91  39884.99  38605.35  33159.50 
##       314       315       316       317       318       319       320       321 
##  36949.19  36129.42  39966.10  37947.79  63769.21  61607.44  63233.23  71242.25 
##       322       323       324       325       326       327       328       329 
##  73632.83  99149.22  97524.67  73321.92  72118.13  70473.21  62413.96  59532.27 
##       330       331       332       333       334       335       336       337 
##  29439.66  33132.25  33572.38  35895.48  35235.42  41010.31  42087.38  37328.46 
##       338       339       340       341       342       343       344       345 
##  36541.80  36668.64  31981.20  37988.40  38652.71  38911.52  39788.52  41576.90 
##       346       347       348       349       350       351       352       353 
##  43401.39  43152.71  36087.68  26642.56  31994.57  30854.01  30443.50  28056.67 
##       354       355       356       357       358       359       360       361 
##  32741.99  36501.44  40971.19  39157.70  40419.72  42561.12  49966.56  50517.90 
##       362       363       364       365       366       367       368       369 
##  50719.85  53186.41  50666.36  50110.14  42745.20  39938.11  36109.80  33885.42 
##       370       371       372       373       374       375       376       377 
##  29937.77  37235.73  39525.85  47422.27  41385.58  40831.98  39367.26  38899.39 
##       378       379       380       381       382       383       384       385 
##  29754.45  34358.91  27400.98  35631.68  45979.05  49549.42  47820.41  49814.85 
##       386       387       388       389       390       391       392       393 
##  56043.61  65542.21  58744.11  53204.66  52932.98  60322.79  60846.90  69527.60 
##       394       395       396       397       398       399       400       401 
##  58618.15  60187.21  59746.71  59229.85  57714.65  56474.49  43214.69  51812.29 
##       402       403       404       405       406       407       408       409 
##  50811.32  49776.11  56186.32  48718.41  48028.60  46352.94  42024.15  40853.22 
##       410       411       412       413       414       415       416       417 
##  38914.39  33000.29  40884.09  43814.52  38479.39  33560.76  48453.23  52303.41 
##       418       419       420       421       422       423       424       425 
##  56238.18  48696.79  45014.94  43689.06  47280.56  35683.42  35412.24  29662.97 
##       426       427       428       429       430       431       432       433 
##  35260.66  43599.96  50501.78  47262.69  44327.26  41248.11  41136.15  37609.00 
##       434       435       436       437       438       439       440       441 
##  33741.96  30972.11  32550.42  34402.40  32404.21  37277.22  43491.49  40239.27 
##       442       443       444       445       446       447       448       449 
##  39944.85  42955.51  40838.60  44833.74  40073.94  31093.57  29929.40  41302.35 
##       450       451       452       453       454       455       456       457 
##  40983.44  46642.61  42272.58  42622.73  44245.53  47968.99  37828.94  42685.09 
##       458       459       460       461       462       463       464       465 
##  38101.87  45681.63  49206.65  51830.91  48555.83  50899.92  51101.74  52851.25 
##       466       467       468       469       470       471       472       473 
##  52348.14  55296.07  52620.57  57678.91  50929.29  48536.83  47120.98  43740.54 
##       474       475       476       477       478       479       480       481 
##  47502.00  54975.91  49394.97  51100.62  45882.07  44258.78  47095.73  36494.94 
##       482       483       484       485       486       487       488       489 
##  30047.36  31919.34  34619.84  36111.92  37079.15  30710.93  43258.93  49928.57 
##       490       491       492       493       494       495       496       497 
##  56768.54  51472.51  56312.41  63956.91  67773.72  54010.56  44575.06  42597.24 
##       498       499       500       501       502       503       504       505 
##  42920.90  43713.18  38192.09  40594.42  45903.44  51593.10  52303.78  52414.50 
##       506       507       508       509       510       511       512       513 
##  46107.51  47449.39  43703.31  46462.66  46127.88  39834.76  40967.44  40141.42 
##       514       515       516       517       518       519       520       521 
##  41248.61  43892.38  36722.38  31995.07  55918.52  64090.74  67748.85  61118.57 
##       522       523       524       525       526       527       528       529 
##  62459.95  76064.78  83051.33  57966.20  52829.01  49518.68  53903.84  53409.47 
##       530       531       532       533       534       535       536       537 
##  43579.21  48578.07  61256.41  55769.56  59171.99  63165.38  60213.83  55237.59 
##       538       539       540       541       542       543       544       545 
##  48690.66  47344.66  55292.95  55043.02  47592.60  49819.35  49645.77  50340.58 
##       546       547       548       549       550       551       552       553 
##  40976.44  32800.72  37148.63  45276.62  45073.30  46781.57  40782.99  49808.60 
##       554       555       556       557       558       559       560       561 
##  50965.53  40730.38  50284.72  58158.52  57522.58  61125.32  56886.63  68663.59 
##       562       563       564       565       566       567       568       569 
##  85375.56  75452.70  64000.27  68425.66  66547.43  67735.73  59293.96  43260.68 
##       570       571       572       573       574       575       576       577 
##  50279.10  56192.45  57376.87  59455.29  60103.23  57225.66  69489.36  58847.96 
##       578       579       580       581       582       583       584       585 
##  52562.21  60174.34  61680.42  54764.85  61040.09  56610.08  53650.53  67239.49 
##       586       587       588       589       590       591       592       593 
##  52647.81  60002.26  59093.39  52806.89  52111.09  52387.51  43090.48  45891.19 
##       594       595       596       597       598       599       600       601 
##  40510.20  44761.88  53537.31  46859.30  52726.41  55107.50  60784.42  56937.24 
##       602       603       604       605       606       607       608       609 
##  61757.64  53314.62  55197.22  55974.63  58285.99  58860.83  58400.96  52570.33 
##       610       611       612       613       614       615       616       617 
##  59641.73  57699.15  54792.34  51493.88  44447.10  55973.26  59866.05  50788.95 
##       618       619       620       621       622       623       624       625 
##  61205.30  65396.01  58876.82  81134.99  66237.27  58556.04  60561.73  55908.65 
##       626       627       628       629       630       631       632       633 
##  46230.73  56952.86  37482.41  37342.45  46923.91  57420.98  55462.90  84231.38 
##       634       635       636       637       638       639       640       641 
##  74404.12  76607.88  78247.42  72966.52  65675.80  62305.49  50193.87  48559.95 
##       642       643       644       645       646       647       648       649 
##  47450.01  45930.18  44312.64  46993.14  51616.85  66608.17  81052.14  78171.57 
##       650       651       652       653       654       655       656       657 
##  79076.56  84956.05  98419.05  93170.51  63400.06  60848.40  57798.62  58784.60 
##       658       659       660       661       662       663       664       665 
##  55220.72  45620.40  48007.98  52331.65  51522.75  63100.27  62977.80  63267.72 
##       666       667       668       669       670       671       672       673 
##  51707.45  53068.82  54088.66  49421.09  43392.64  46431.67  44027.21  47518.74 
##       674       675       676       677       678       679       680       681 
##  45268.12  38095.99  32746.74  32744.24  35496.97  40235.40  42498.89  40500.70 
##       682       683       684       685       686       687       688       689 
##  40524.32  40973.69  35308.40  41646.38  41143.14  41442.81  43441.00  54163.26 
##       690       691       692       693       694       695       696       697 
##  62632.03  70692.15  59974.52  55976.88  52855.00  58016.06  48295.77  41986.28 
##       698       699       700       701       702       703       704       705 
##  35873.74  32588.40  31066.33  36580.42  34841.65  35515.21  41456.18  70154.43 
##       706       707       708       709       710       711       712       713 
##  76321.33  93892.18  90206.46  92805.24 107850.66 106689.74  84021.06  84368.84 
##       714       715       716       717       718       719       720       721 
##  75695.51  72794.32  70768.13  53474.58  48866.74  52363.51  49864.96  38965.50 
##       722       723       724       725       726       727       728       729 
##  44540.95  40807.24  43055.36  40927.70  31622.55  35578.57  36204.65  29831.67 
##       730       731       732       733       734       735       736       737 
##  47923.50  50174.13  48205.42  53866.85  46263.34  46538.26  54530.29  40963.82 
##       738       739       740       741       742       743       744       745 
##  37371.07  45883.57  42653.85  44158.43  46930.78  46042.78  42457.15  49455.20 
##       746       747       748       749       750       751       752       753 
##  44469.72  65459.61  70786.88  66891.09  58815.84  78620.82  71686.00  70603.80 
##       754       755       756       757       758       759       760       761 
##  55821.68 104605.57 121698.43 126293.40 107795.18 110226.63 109473.09 107499.76 
##       762       763       764       765       766       767       768       769 
##  60115.85  45147.40  47059.12  45681.88  43655.82 152368.62 156811.26 182048.35 
##       770       771       772       773       774       775       776       777 
## 185619.48 179549.29 177544.10 184435.18  81519.63  72096.64  38427.78  41865.19 
##       778       779       780       781       782       783       784       785 
##  42274.70  46678.10  41069.66  41390.08  41232.49  45791.97  40522.44  40219.53 
##       786       787       788       789       790       791       792       793 
##  45340.10  48368.75  46620.99  43966.86  46709.09  50081.03  50487.41  45024.06 
##       794       795       796       797       798       799       800       801 
##  41054.92  41640.63  42051.27  36676.89  36758.74  36030.44  35920.98  47538.11 
##       802       803       804       805       806       807       808       809 
##  50270.85  56891.88  59043.15  53600.42  60771.79  68515.64  57371.74  50436.93 
##       810       811       812       813 
##  44281.27  48096.20  52469.99  50638.37 
## 
## $shapiro.test
## [1] 0
## 
## $levenes.test
## [1] 0
## 
## $autcorr
## [1] "No autocorrelation evidence"
## 
## $post_sums
## [1] "Post-Est Warning"
## 
## $adjr_sq
## [1] 0.8115
## 
## $fstat.bootstrap
## 
## ORDINARY NONPARAMETRIC BOOTSTRAP
## 
## 
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~ 
##     ., parallel = parr)
## 
## 
## Bootstrap Statistics :
##        original      bias    std. error
## t1*    4.925198   0.7604988    3.838791
## t2* 2622.788919 165.3959770  870.065092
## WARNING: All values of t3* are NA
## 
## $itsa.plot
## 
## $booted.ints
##       Parameter    Lower CI Median F-value   Upper CI
## 1 interrupt_var    1.045771       4.862488   13.02008
## 2    lag_depvar 1588.446744    2663.618777 4408.51802

Ahora con las tendencias descompuestas

require(zoo)
require(scales)
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>% 
    dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
                  gasto=="aspiradora"~"Electrodomésticos/mantención casa",
                  gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
                  gasto=="Tina"~"Electrodomésticos/mantención casa",
                  gasto=="Nexium"~"Farmacia",
                  gasto=="donaciones"~"Donaciones/regalos",
                  gasto=="Regalo chocolates"~"Donaciones/regalos",
                  gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
                  gasto=="Chromecast"~"Electrodomésticos/mantención casa",
                  gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
                  gasto=="Vacuna Influenza"~"Farmacia",
                  gasto=="Easy"~"Electrodomésticos/mantención casa",
                  gasto=="Sopapo"~"Electrodomésticos/mantención casa",
                  gasto=="filtro agua"~"Electrodomésticos/mantención casa",
                  gasto=="ropa tami"~"Donaciones/regalos",
                  gasto=="yaz"~"Farmacia",
                  gasto=="Yaz"~"Farmacia",
                  gasto=="Remedio"~"Farmacia",
                  gasto=="Entel"~"VTR",
                  gasto=="Kerosen"~"Gas/Bencina",
                  gasto=="Parafina"~"Gas/Bencina",
                  gasto=="Plata basurero"~"Donaciones/regalos",
                  gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
                  gasto=="Wild Protein"~"Comida",
                  gasto=="Granola Wild Foods"~"Comida",
                  gasto=="uber"~"Transporte",
                  gasto=="Uber Reñaca"~"Transporte",
                  gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
                  gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
                  gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
                  gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
                  gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
                  gasto=="Reloj"~"Electrodomésticos/mantención casa",
                  gasto=="Arreglo"~"Electrodomésticos/mantención casa",
                  gasto=="Pan Pepperino"~"Comida",
                  gasto=="Cookidoo"~"Comida",
                  gasto=="remedios"~"Farmacia",
                  gasto=="Bendina Reñaca"~"Gas/Bencina",
                  gasto=="Bencina Reñaca"~"Gas/Bencina",
                  gasto=="Vacunas Influenza"~"Farmacia",
                  gasto=="Remedios"~"Farmacia",
                  gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
                  #2024
                  gasto=="cartero"~"Correo",
                  gasto=="correo"~"Correo",
                  gasto=="Gaviscón y Paracetamol"~"Farmacia",
                  gasto=="Regalo Matri Cony"~"Donaciones/regalos",
                  gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
                  gasto=="Aporte Basureros"~"Donaciones/regalos",
                  gasto=="donación"~"Donaciones/regalos",
                  gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
                  gasto=="basureros"~"Donaciones/regalos",
                  gasto=="Microondas regalo"~"Donaciones/regalos",
                  gasto=="Cruz Verde"~"Farmacia",
                  gasto=="Remedios Covid"~"Farmacia",      
                  gasto=="nacho"~"Electrodomésticos/mantención casa",
                  gasto=="Jardinero"~"Electrodomésticos/mantención casa",
                  gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
                  gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",      
                  gasto=="Uber cumple papá"~"Transporte",
                  gasto=="Uber"~"Transporte",
                  gasto=="Uber Matri Cony"~"Transporte",
                  gasto=="Bencina + tag"~"Gas/Bencina",
                  gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
                  gasto=="Bencina + peajes Maite"~"Gas/Bencina",
                  gasto=="Crunchyroll"~"Netflix",
                  gasto=="Crunchyroll"~"Netflix",
                  gasto=="Incoludido"~"Enceres",
                  gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
                  gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
                  gasto=="Brussels"~"Comida",
                  gasto=="Tres toques"~"Enceres",
                  gasto=="Transferencia"~"Otros",
                  gasto=="prestamo"~"Otros",
                  gasto=="Préstamo Andrés"~"Otros",
                  gasto=="mouse"~"Otros",
                  gasto=="lamina"~"Otros",
      T~gasto)) %>% 
    dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
    #dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>% 
#    dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de  diosi. Junio 24, 2019   
    dplyr::summarise(monto=sum(monto)) %>% 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
  ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(size=1) +
  facet_grid(gasto~.)+
  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
  ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
  guides(color = F)+
  theme_custom() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

# Apply MSTL decomposition
mstl_data_autplt <- forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start = 
lubridate::decimal_date(as.Date("2019-03-03")))

# Convert the decomposed time series to a data frame
mstl_df <- data.frame(
  Date = as.Date(Gastos_casa$fecha, format="%d/%m/%Y"),
  Data = as.numeric(mstl_data_autplt[, "Data"]),
  Trend = as.numeric(mstl_data_autplt[, "Trend"]),
  Remainder = as.numeric(mstl_data_autplt[, "Remainder"])
)

# Reshape the data frame for ggplot2
mstl_long <- mstl_df %>%
  pivot_longer(cols = -Date, names_to = "Component", values_to = "Value")

# Plotting with ggplot2
ggplot(mstl_long, aes(x = Date, y = Value)) +
  geom_line() +
  theme_bw() + 
  labs(title = "Descomposición MSTL", x = "Fecha", y = "Valor") +
  scale_x_date(date_breaks = "3 months", date_labels = "%m-%Y") +
  facet_wrap(~ Component, scales = "free_y", ncol = 1) +
  theme(strip.text = element_text(size = 12),
        axis.text.x = element_text(angle = 90, hjust = 1))

library(bsts)
library(CausalImpact)
ts_week_covid<-  
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(fecha_week)%>%
    dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
    dplyr::ungroup() %>% 
    dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
    data.frame()


ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]
##  [1]  98.357   4.780  56.784  50.506  64.483  67.248  49.299  35.786  58.503
## [10]  64.083  20.148  73.476 127.004  81.551  69.599 134.446  58.936  26.145
## [19] 129.927 104.989 130.860  81.893  95.697  64.579 303.471 151.106  49.275
## [28]  76.293  33.940  83.071 119.512  20.942  58.055  71.728  44.090  33.740
## [37]  59.264  77.410  60.831  63.376  48.754 235.284  29.604 115.143  72.419
## [46]   5.980  80.063 149.178  69.918 107.601  72.724  63.203  99.681 130.309
## [55] 195.898 112.066
# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na, 
               state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
               family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
               niter = 20000, 
               #burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
               seed= 2125)
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#,
#               dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")

impact2d1 <- CausalImpact(bsts.model = model1d1,
                       post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
  ylab("Monto Semanal (En miles)")

burn1d1 <- SuggestBurn(0.1, model1d1)
corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d  <- tm_map(corpus, tolower)
d  <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq, 
          max.words=100, random.order=FALSE, rot.per=0.35, 
          colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")

fit_month_gasto <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
                  gasto=="aspiradora"~"Electrodomésticos/mantención casa",
                  gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
                  gasto=="Tina"~"Electrodomésticos/mantención casa",
                  gasto=="Nexium"~"Farmacia",
                  gasto=="donaciones"~"Donaciones/regalos",
                  gasto=="Regalo chocolates"~"Donaciones/regalos",
                  gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
                  gasto=="Chromecast"~"Electrodomésticos/mantención casa",
                  gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
                  gasto=="Vacuna Influenza"~"Farmacia",
                  gasto=="Easy"~"Electrodomésticos/mantención casa",
                  gasto=="Sopapo"~"Electrodomésticos/mantención casa",
                  gasto=="filtro agua"~"Electrodomésticos/mantención casa",
                  gasto=="ropa tami"~"Donaciones/regalos",
                  gasto=="yaz"~"Farmacia",
                  gasto=="Yaz"~"Farmacia",
                  gasto=="Remedio"~"Farmacia",
                  gasto=="Entel"~"VTR",
                  gasto=="Kerosen"~"Gas/Bencina",
                  gasto=="Parafina"~"Gas/Bencina",
                  gasto=="Plata basurero"~"Donaciones/regalos",
                  gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
                  gasto=="Wild Protein"~"Comida",
                  gasto=="Granola Wild Foods"~"Comida",
                  gasto=="uber"~"Transporte",
                  gasto=="Uber Reñaca"~"Transporte",
                  gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
                  gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
                  gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
                  gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
                  gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
                  gasto=="Reloj"~"Electrodomésticos/mantención casa",
                  gasto=="Arreglo"~"Electrodomésticos/mantención casa",
                  gasto=="Pan Pepperino"~"Comida",
                  gasto=="Cookidoo"~"Comida",
                  gasto=="remedios"~"Farmacia",
                  gasto=="Bendina Reñaca"~"Gas/Bencina",
                  gasto=="Bencina Reñaca"~"Gas/Bencina",
                  gasto=="Vacunas Influenza"~"Farmacia",
                  gasto=="Remedios"~"Farmacia",
                  gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
                  #2024
                  gasto=="cartero"~"Correo",
                  gasto=="correo"~"Correo",
                  gasto=="Gaviscón y Paracetamol"~"Farmacia",
                  gasto=="Regalo Matri Cony"~"Donaciones/regalos",
                  gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
                  gasto=="Aporte Basureros"~"Donaciones/regalos",
                  gasto=="donación"~"Donaciones/regalos",
                  gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
                  gasto=="basureros"~"Donaciones/regalos",
                  gasto=="Microondas regalo"~"Donaciones/regalos",
                  gasto=="Cruz Verde"~"Farmacia",
                  gasto=="Remedios Covid"~"Farmacia",      
                  gasto=="nacho"~"Electrodomésticos/mantención casa",
                  gasto=="Jardinero"~"Electrodomésticos/mantención casa",
                  gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
                  gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",      
                  gasto=="Uber cumple papá"~"Transporte",
                  gasto=="Uber"~"Transporte",
                  gasto=="Uber Matri Cony"~"Transporte",
                  gasto=="Bencina + tag"~"Gas/Bencina",
                  gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
                  gasto=="Bencina + peajes Maite"~"Gas/Bencina",
                  gasto=="Crunchyroll"~"Netflix",
                  gasto=="Crunchyroll"~"Netflix",
                  gasto=="Incoludido"~"Enceres",
                  gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
                  gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
                  gasto=="Brussels"~"Comida",
                  gasto=="Tres toques"~"Enceres",
                  gasto=="Transferencia"~"Otros",
                  gasto=="prestamo"~"Otros",
                  gasto=="Préstamo Andrés"~"Otros",
                  gasto=="mouse"~"Otros",
                  gasto=="lamina"~"Otros",
      T~gasto)) %>% 
  dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>% 
  dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>% 
    dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
    dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
  data.frame() %>% na.omit()

fit_month_gasto_25<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2025",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_24<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2024",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_23<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2023",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_22<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2022",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_21<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2021",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()


fit_month_gasto_20<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2020",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame() %>% ungroup()

fit_month_gasto_25 %>% 
dplyr::right_join(fit_month_gasto_24,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>% 
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>% 
  janitor::adorn_totals() %>% 
  #dplyr::select(-3)%>% 
  knitr::kable(format = "markdown", size=12, col.names= c("Item","2025","2024","2023","2022","2021","2020"))
Item 2025 2024 2023 2022 2021 2020
Agua 0.000 6.993667 5.195333 5.410333 5.849167 9.93775
Comida 325.252 326.890000 366.009167 312.386750 317.896583 392.93367
Comunicaciones 0.000 0.000000 0.000000 0.000000 0.000000 0.00000
Electricidad 55.000 83.582750 38.104750 47.072333 29.523000 20.60458
Enceres 0.000 23.989000 18.259750 24.219750 14.801167 39.01200
Farmacia 0.000 0.000000 10.704083 2.835000 13.996083 14.03675
Gas/Bencina 0.000 44.292667 42.636000 45.575000 13.583667 17.25833
Diosi 73.999 33.319583 55.804250 31.180667 52.687833 37.12133
donaciones/regalos 0.000 0.000000 0.000000 0.000000 0.000000 0.00000
Electrodomésticos/ Mantención casa 0.000 0.000000 0.000000 0.000000 0.000000 0.00000
VTR 21.990 18.326667 12.829167 25.156667 19.086917 19.11375
Netflix 0.000 1.391417 8.713833 7.151583 7.028750 8.24725
Otros 0.000 76.164000 5.481667 5.000000 0.000000 0.00000
Total 476.241 614.949750 563.738000 505.988083 474.453167 558.26542
## Joining with `by = join_by(word)`


2. UF Proyectada

Saqué la UF proyectada

#options(max.print=5000)

uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")
uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table")

tryCatch(uf25 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2025.htm")%>% rvest::html_nodes("table"),
    error = function(c) {
      uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
      
    }
  )

tryCatch(uf25 <-uf25[[length(uf25)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
    error = function(c) {
      uf25 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
    }
)

uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2024, uf23[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2025, uf25)
)

uf_serie_corrected<-
uf_serie %>% 
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>% 
  dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>% 
  dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>% 
   na.omit()#%>%  dplyr::filter(is.na(date3))
## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
##   = T)`.
## Caused by warning:
## !  54 failed to parse.
#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)

warning(paste0("number of observations:",nrow(uf_serie_corrected),",  min uf: ",min(uf_serie_corrected$value),",  min date: ",min(uf_serie_corrected $date3 )))
## Warning: number of observations:2624, min uf: 26799.01, min date: 2018-01-01
# 
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>% 
#   dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))

ts_uf_proy<-
ts(data = uf_serie_corrected$value, 
   start = as.numeric(as.Date("2018-01-01")), 
   end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats <- forecast::tbats(ts_uf_proy)
    

fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)
# Configurar API Key
nixtlar::nixtla_set_api_key(Sys.getenv("API_NIXTLA"))
## API key has been set for the current session.
try(nixtlar::nixtla_set_api_key(Sys.getenv("NIXTLA")))
## API key has been set for the current session.
# Preparar datos en formato requerido por TimeGPT
uf_timegpt <- uf_serie_corrected %>%
    dplyr::rename(ds = date3, y = value) %>%
    dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
    dplyr::mutate(unique_id = "serie_1")%>%
    dplyr::select(unique_id, ds, y)

# Realizar pronóstico con TimeGPT
timegpt_fcst <- nixtlar::nixtla_client_forecast(
  uf_timegpt,
  h = 298,               # 298 días a pronosticar
  freq = "D",            # Frecuencia diaria
  add_history = TRUE,     # Incluir datos históricos en el output
  level = c(80,95),
  model=  "timegpt-1-long-horizon", 
  clean_ex_first = TRUE
)
## The specified horizon h exceeds the model horizon. This may lead to less accurate forecasts. Please consider using a smaller horizon.
# 1. Convertir 'ds' a fecha en ambas tablas
uf_timegpt <- uf_timegpt %>% 
    mutate(ds = as.Date(ds))

timegpt_fcst <- timegpt_fcst %>% 
    mutate(ds = as.Date(ds))

# 2. Combinar los datos históricos y el pronóstico
full_data <- bind_rows(
    uf_timegpt %>% mutate(type = "Histórico"),
    timegpt_fcst %>% mutate(type = "Pronóstico")
)

# Visualizar resultados
ggplot(full_data, aes(x = ds, y = TimeGPT)) +
    # Intervalo de confianza del 95%
    geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`), 
                fill = "#4B9CD3", alpha = 0.2) +
    # Intervalo de confianza del 80%
    geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`), 
                fill = "#4B9CD3", alpha = 0.3) +
    # Línea histórica
    geom_line(data = filter(full_data, type == "Histórico"), 
              aes(color = "Histórico"), size = 1) +
    # Línea de pronóstico
    geom_line(data = filter(full_data, type == "Pronóstico"), 
              aes(color = "Pronóstico"), size = 1) +
    # Línea vertical separadora
    geom_vline(xintercept = max(filter(full_data, type == "Histórico")$ds), 
               linetype = "dashed", color = "red", size = 0.8) +
    # Configuración del eje x
    scale_x_date(
        date_breaks = "3 months",  # Reduce la frecuencia de las etiquetas
        date_labels = "%b %Y",  # Formato de etiquetas (mes y año)
    ) +
    # Configuración del eje y
    scale_y_continuous(labels = function(x) format(x, scientific = FALSE)) +
    # Configuración de colores
    scale_color_manual(
        name = "Leyenda",
        values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
    ) +
    # Títulos y subtítulos
    labs(
        title = "Pronóstico de Serie Temporal con TimeGPT",
        subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
        x = "Fecha",
        y = "Valor",
        color = "Leyenda"
    ) +
    # Tema y estilos
    theme_minimal() +
    theme(
        axis.text.x = element_text(angle = 45, hjust = 1, size = 8),
        axis.title.x = element_text(size = 10),
        axis.title.y = element_text(size = 10),
        legend.position = "bottom",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()
    )
## Warning: Removed 2624 rows containing missing values or values outside the scale range
## (`geom_line()`).

library(prophet)
## Warning: package 'prophet' was built under R version 4.4.2
## Loading required package: Rcpp
## Warning: package 'Rcpp' was built under R version 4.4.2
## Loading required package: rlang
## Warning: package 'rlang' was built under R version 4.4.2
## 
## Attaching package: 'rlang'
## The following objects are masked from 'package:purrr':
## 
##     %@%, flatten, flatten_chr, flatten_dbl, flatten_int, flatten_lgl,
##     flatten_raw, invoke, splice
## The following object is masked from 'package:sparklyr':
## 
##     invoke
## The following object is masked from 'package:data.table':
## 
##     :=
  model <- prophet(
  cbind.data.frame(ds= as.Date(uf_timegpt$ds), y=uf_timegpt$y),
  # Trend flexibility
  growth = "linear",
  changepoint.prior.scale = 0.05,  # Reduced for smoother trend
  n.changepoints = 50,  # Increased from default 25
  
  # Seasonality
  yearly.seasonality = TRUE,
  weekly.seasonality = TRUE,
  daily.seasonality = FALSE,  # Disabled for daily data
  seasonality.mode = "additive",
  seasonality.prior.scale = 15,  # Increased to capture stronger seasonality
  
  # Holidays (if applicable)
  # holidays = generated_holidays  # Create with add_country_holidays()
  
  # Uncertainty intervals
  interval.width = 0.95,
  uncertainty.samples = 1000
)
future <- make_future_dataframe(model, periods = 298, include_history = T)
forecast <- predict(model, future)
forecast <- forecast[, c("ds", "yhat", "yhat_lower", "yhat_upper")]
forecast$pred <- ifelse(forecast$ds > max(uf_timegpt$ds), 1,0)
## Warning in check_tzones(e1, e2): 'tzone' attributes are inconsistent
forecast$ds <- as.Date(forecast$ds)

ggplot(forecast, aes(x = ds, y = yhat)) +
  geom_ribbon(aes(ymin = yhat_lower, ymax = yhat_upper), 
              fill = "#9ecae1", alpha = 0.4) +
  geom_line(color = "#08519c", linewidth = 0.8) +
  geom_vline(xintercept = max(uf_timegpt$ds), color = "red", linetype = "dashed", linewidth=1) +
  scale_x_date(date_breaks = "6 months", date_labels = "%y %b") +
  scale_y_continuous(labels = scales::comma) +
  labs(title = "Valores predichos (95%IC)",
      # subtitle = "March 10, 2025 - May 7, 2025",
       x = "Fecha",
       y = "Valor",
      # caption = "Source: Prophet Forecast Model"
      ) +
  theme_minimal() +
  theme(
    plot.title = element_text(face = "bold", size = 14),
    plot.subtitle = element_text(color = "gray50"),
    axis.text.x = element_text(angle = 45, hjust = 1),
    panel.grid.minor = element_blank(),
    panel.border = element_blank(),
    plot.caption = element_text(color = "gray30")
  )

La proyección de la UF a 298 días más 2025-03-09 00:04:58 sería de: 26.921 pesos// Percentil 95% más alto proyectado: 35.097,94

Según prophet: La proyección de la UF a 298 días más 2026-01-01 sería de: 38.446 pesos// Percentil 95% más alto proyectado: 40.771

Ahora con un modelo ARIMA automático


arima_optimal_uf = forecast::auto.arima(ts_uf_proy)

  autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq(from = as.Date("2018-01-01"), 
                                  to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)), 
      tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")), 
                             to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
      tickmode = "array",
    tickangle = 90
    ))
fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)
dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
               col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales
Item UF Proyectada (TBATS) UF Proyectada (ARIMA)
Lo.95 26537.82 26331.66
Lo.80 26669.99 26493.43
Point.Forecast 26921.46 26799.01
Hi.80 31593.20 32074.20
Hi.95 34386.03 34866.72


3. Gastos proyectados

Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.

Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
                               col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
                                             "link"),skip=1) %>% 
              dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
              dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
              dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
              dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
              data.frame()

uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>%  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
              data.frame() %>% 
  dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found

ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)], 
   start = 1, 
   end = nrow(uf_serie_corrected_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)], 
   start = 1, 
   end = nrow(Gastos_casa_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)

seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")

autplo2t<-
  autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t

Ahora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.

paste0("Optimo pero sin regresor")
## [1] "Optimo pero sin regresor"
arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal
## Series: ts_gastos_casa_m 
## ARIMA(0,1,2) 
## 
## Coefficients:
##           ma1      ma2
##       -0.5565  -0.3087
## s.e.   0.1110   0.1126
## 
## sigma^2 = 37449:  log likelihood = -474.19
## AIC=954.39   AICc=954.75   BIC=961.18
paste0("Optimo pero con regresor")
## [1] "Optimo pero con regresor"
arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2
## Series: ts_gastos_casa_m 
## Regression with ARIMA(0,0,1) errors 
## 
## Coefficients:
##          ma1  intercept     xreg
##       0.3642   463.3959  18.3018
## s.e.  0.1004   275.1981   8.4991
## 
## sigma^2 = 35487:  log likelihood = -477.87
## AIC=963.74   AICc=964.34   BIC=972.85
forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>% 
  dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>% 
  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
            data.frame()
autplo2t2<-
  autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t2
fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))

dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
               col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) 
## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales
Item Modelo ARIMA con regresor (UF) Modelo ARIMA sin regresor Modelo TBATS
Lo.95 700.1710 706.9536 742.9974
Lo.80 836.1806 850.5687 836.9886
Point.Forecast 1093.1089 1121.8640 1059.2415
Hi.80 1350.0372 1413.0247 1339.9033
Hi.95 1486.0468 1567.1559 1517.1589


4. Gastos mensuales (resumen manual)

path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")

Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
                #col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
                skip=0)
## Rows: 80 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(Gastos_casa_mensual_2022,5) %>% 
  knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")
Resumen mensual, primeras 5 observaciones
n mes_ano Tami Andrés
1 marzo_2019 175533 68268
2 abril_2019 152640 55031
3 mayo_2019 152985 192219
4 junio_2019 291067 84961
5 julio_2019 241389 205893


(
Gastos_casa_mensual_2022 %>% 
    reshape2::melt(id.var=c("n","mes_ano")) %>%
  dplyr::mutate(gastador=as.factor(variable)) %>% 
  dplyr::select(-variable) %>% 
 ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
  scale_color_manual(name="Gastador", values=c("red", "blue"))+
  geom_line(size=1) +
  #geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
  ggtitle( "Gastos Mensuales (total manual)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
#  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
#  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
 # guides(color = F)+
  theme_custom() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )
) %>% ggplotly()


Session Info

Sys.getenv("R_LIBS_USER")
## [1] "D:\\a\\_temp\\Library"
sessionInfo()
## R version 4.4.0 (2024-04-24 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows Server 2022 x64 (build 20348)
## 
## Matrix products: default
## 
## 
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252  LC_CTYPE=Spanish_Chile.1252   
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C                  
## [5] LC_TIME=Spanish_Chile.1252    
## system code page: 65001
## 
## time zone: UTC
## tzcode source: internal
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] prophet_1.0         rlang_1.1.5         Rcpp_1.0.14        
##  [4] CausalImpact_1.3.0  bsts_0.9.10         BoomSpikeSlab_1.2.6
##  [7] Boom_0.9.15         scales_1.3.0        ggiraph_0.8.12     
## [10] tidytext_0.4.2      DT_0.33             janitor_2.2.1      
## [13] autoplotly_0.1.4    rvest_1.0.4         plotly_4.10.4      
## [16] xts_0.14.1          forecast_8.23.0     wordcloud_2.6      
## [19] RColorBrewer_1.1-3  SnowballC_0.7.1     tm_0.7-15          
## [22] NLP_0.3-2           tsibble_1.1.6       lubridate_1.9.4    
## [25] forcats_1.0.0       dplyr_1.1.4         purrr_1.0.4        
## [28] tidyr_1.3.1         tibble_3.2.1        tidyverse_2.0.0    
## [31] gsynth_1.2.1        sjPlot_2.8.17       lattice_0.22-6     
## [34] GGally_2.2.1        ggplot2_3.5.1       gridExtra_2.3      
## [37] plotrix_3.8-4       sparklyr_1.8.6      httr_1.4.7         
## [40] readxl_1.4.3        zoo_1.8-12          stringr_1.5.1      
## [43] stringi_1.8.4       DataExplorer_0.8.3  data.table_1.16.4  
## [46] reshape2_1.4.4      fUnitRoots_4040.81  plyr_1.8.9         
## [49] readr_2.1.5        
## 
## loaded via a namespace (and not attached):
##   [1] bitops_1.0-9        cellranger_1.1.0    datawizard_1.0.0   
##   [4] httr2_1.1.0         lifecycle_1.0.4     StanHeaders_2.32.10
##   [7] doParallel_1.0.17   globals_0.16.3      vroom_1.6.5        
##  [10] MASS_7.3-60.2       insight_1.0.2       crosstalk_1.2.1    
##  [13] magrittr_2.0.3      sass_0.4.9          rmarkdown_2.29     
##  [16] jquerylib_0.1.4     yaml_2.3.10         fracdiff_1.5-3     
##  [19] doRNG_1.8.6.1       askpass_1.2.1       pkgbuild_1.4.6     
##  [22] DBI_1.2.3           abind_1.4-8         quadprog_1.5-8     
##  [25] nnet_7.3-19         rappdirs_0.3.3      sandwich_3.1-1     
##  [28] inline_0.3.21       tokenizers_0.3.0    listenv_0.9.1      
##  [31] anytime_0.3.11      performance_0.13.0  spatial_7.3-17     
##  [34] parallelly_1.42.0   codetools_0.2-20    xml2_1.3.6         
##  [37] tidyselect_1.2.1    ggeffects_2.2.0     farver_2.1.2       
##  [40] urca_1.3-4          its.analysis_1.6.0  matrixStats_1.5.0  
##  [43] stats4_4.4.0        jsonlite_1.8.9      ellipsis_0.3.2     
##  [46] Formula_1.2-5       iterators_1.0.14    systemfonts_1.2.1  
##  [49] foreach_1.5.2       tools_4.4.0         glue_1.8.0         
##  [52] xfun_0.50           TTR_0.24.4          ggfortify_0.4.17   
##  [55] loo_2.8.0           withr_3.0.2         timeSeries_4041.111
##  [58] fastmap_1.2.0       boot_1.3-30         openssl_2.3.2      
##  [61] caTools_1.18.3      digest_0.6.37       timechange_0.3.0   
##  [64] R6_2.6.1            lfe_3.1.1           colorspace_2.1-1   
##  [67] networkD3_0.4       gtools_3.9.5        generics_0.1.3     
##  [70] htmlwidgets_1.6.4   ggstats_0.8.0       pkgconfig_2.0.3    
##  [73] gtable_0.3.6        timeDate_4041.110   lmtest_0.9-40      
##  [76] selectr_0.4-2       janeaustenr_1.0.0   htmltools_0.5.8.1  
##  [79] carData_3.0-5       tseries_0.10-58     snakecase_0.11.1   
##  [82] knitr_1.49          rstudioapi_0.17.1   tzdb_0.4.0         
##  [85] uuid_1.2-1          nlme_3.1-164        curl_6.2.0         
##  [88] cachem_1.1.0        sjlabelled_1.2.0    KernSmooth_2.23-22 
##  [91] parallel_4.4.0      fBasics_4041.97     pillar_1.10.1      
##  [94] vctrs_0.6.5         gplots_3.2.0        slam_0.1-55        
##  [97] car_3.1-3           dbplyr_2.5.0        xtable_1.8-4       
## [100] evaluate_1.0.3      mvtnorm_1.3-3       cli_3.6.4          
## [103] compiler_4.4.0      crayon_1.5.3        rngtools_1.5.2     
## [106] future.apply_1.11.3 labeling_0.4.3      sjmisc_2.8.10      
## [109] rstan_2.32.6        QuickJSR_1.5.1      viridisLite_0.4.2  
## [112] assertthat_0.2.1    munsell_0.5.1       lazyeval_0.2.2     
## [115] Matrix_1.7-0        sjstats_0.19.0      hms_1.1.3          
## [118] bit64_4.6.0-1       future_1.34.0       nixtlar_0.6.2      
## [121] extraDistr_1.10.0   igraph_2.1.4        RcppParallel_5.1.10
## [124] bslib_0.9.0         quantmod_0.4.26     bit_4.5.0.1
#save.image("__analisis.RData")

sesion_info <- devtools::session_info()
dplyr::select(
  tibble::as_tibble(sesion_info$packages),
  c(package, loadedversion, source)
) %>% 
  DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
              caption = htmltools::tags$caption(
        style = 'caption-side: top; text-align: left;',
        '', htmltools::em('Packages')),
      options=list(
initComplete = htmlwidgets::JS(
        "function(settings, json) {",
        "$(this.api().tables().body()).css({
            'font-family': 'Helvetica Neue',
            'font-size': '50%', 
            'code-inline-font-size': '15%', 
            'white-space': 'nowrap',
            'line-height': '0.75em',
            'min-height': '0.5em'
            });",#;
        "}")))